gibbonsecr manual v0.0
3 April 2016
1 Overview
The gibbonsecr software package uses Spatially Explicit Capture–Recapture (SECR) methods to estimate the density of gibbon groups from acoustic survey data. This manual begins with a brief introduction to the theory behind SECR and then describes the main components of the user interface.
2 Introduction to SECR
Over the past decade SECR has become an increasingly popular tool for wildlife population assessment and has been used to analyse survey data for a wide range of animal groups. The main advantage of this method over traditional capture-recapture techniques is that it allows direct estimation of population density rather than abundance. Traditional capture-recapture methods can only provide density estimates through the use of separate estimates or assumptions about the size of the sampled area. In SECR however, density is estimated directly from the survey data by using information contained in the pattern of the recaptures to make inferences about the spatial location of animals. By extracting spatial information in this way, SECR provides estimates of density without requiring the exact locations of the detected animals to be known in advance.
2.1 Basic setup
The basic data collection setup for an SECR analysis consists of a spatial array of detectors. Detectors come in a variety of different forms, including traps which physically detain the animals, and proximity detectors which do not. The use of proximity detectors makes it possible for an animal to be detected at more than one detector (i.e. recaptured) during a single sampling occasion.
The plot below shows a hypothetical array of proximity detectors, with red squares representing detections of the same animal (or the same group in the case of gibbon surveys) and black squares representing no detections.
The pattern of the detections (i.e. the pattern of the recapture data) contains information about the true location of the animal/group; an intuitive guess would be that the true location is somewhere near the cluster of red detectors. The plot below shows a set of probability contours for this unknown location, given the recapture data.
In the case of acoustic gibbon surveys the listening posts can be treated as proximity detectors and the same logic can be applied to obtain information on the locations of the detected groups. However, the design shown in the figure above would obviously be impractical for gibbon surveys. The next figure shows probability contours for a more realistic array of listening posts where a group has been detected at two of the posts.
The obvious conclusion here is that using smaller arrays of detectors results in less information about the unknown locations.
2.2 Estimated bearings
SECR also allows supplementary information on group location to be included in the analysis in addition to the recapture data, for example in the form of estimated bearings to the detected animals/groups. The next figure illustrates how taking account of information contained in the estimated bearings can provide better quality information on unknown locations.
Using estimated bearings in this way can lead to density estimates that are less biased and more precise than using recapture data alone. Since the precision of bearing estimates is usually unknown, SECR methods need to estimate it from the data. This requires the choice of a bearing error distribution. The figure below shows two common choices of distribution for modelling bearing errors – the von Mises and the wrapped Cauchy. The colour of the lines in these plots indicate the value of the precision parameter (and which need to be estimated from the survey data).
2.3 Estimated distances
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2.4 Detection functions
Another key feature of SECR is that the probability of detecting a (calling) gibbon group at a given location is modelled as a function of distance between the group and the listening post. This function – referred to as the detection function – is typically assumed to belong to one of two main types of function: the half normal or the hazard rate. The specific shape of the detection function depends on the value of its parameters, which need to be estimated from the survey data. The half normal has two parameters: g0 and sigma: the g0 parameter gives the detection probability at zero distance and the sigma parameter controls the width of the function. The hazard rate has three parameters: g0, sigma and z. The g0 and sigma parameters have the same interpretation as for the half normal, while the z parameter controls the shape of the ‘shoulder’ and adds a greater degree of flexibility. The figure below illustrates the shape of these detection functions for a range of parameter values.
2.5 Detection surface
Associating a detection function with each listening post allows us to calculate the overall probability of detection – i.e. the probability of detection at at least one listening post – for any given location. The figure below illustrates this concept using a heat map of a detection surface where color indicates overall detection probability.
The region near the centre of this surface is close to the listening post array and has the highest detection probability. In this case, an group located close to the detectors will almost certainly be detected. The detection probability decreases as distance from the detectors increases.
2.6 Effective sampling area
The shape of the detection surface is related to the size of the effective sampling area. Since the region close to the detectors has a very high detection probability, most animals/groups within this region will be detected and it will therefore be sampled almost perfectly. However, regions where the detection probability is less than 1 will not be completely sampled as some animal/groups in these areas will be missed. The figure below illustrates this idea for a series of arbitrary detection surfaces.
The first plot in this figure shows a flat surface where the detection probability is 0.5 everywhere. In this scenario every animal/group has a 50% chance of being detected. If the area covered by the surface was 10km2, then the effective sampling area would be 10km2 x 0.5 = 5km2. Using this detection process we would expect to detect the same number of groups as we would if we had perfectly sampled an area of 5km2. In the second plot, half of the area is sampled perfectly and the other half is not sampled at all, so this has the same effective sampling area as the first plot. The third plot has a detection gradient and isn’t as intuitive to interpret. However, the general to calculate the effective survey area is to calculate the volume under the detection surface. The third plot has the same volume as the other two, so it has the same effective area.
3 Loading the software
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3.1 Launch from R
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3.1.1 Install R
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3.1.2 Install prerequisite R packages
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3.1.3 Install the gibbonsecr package
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3.1.4 Launch the user interface
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3.2 Launch from a desktop icon
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3.2.1 Download the files
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3.2.2 Make a shortcut icon
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4 Data import
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4.1 CSV files
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4.1.1 Detections
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4.1.2 Posts
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4.1.3 Covariates
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4.2 Data details
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5 Making a mask
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5.1 Mask size and resolution
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5.1.1 Buffer
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5.1.2 Spacing
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5.2 Shapefiles
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5.2.1 Region
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5.2.2 Habitat
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6 Model fitting
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6.1 Model options
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6.2 Model parameters
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6.2.1 Formulas
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6.2.2 Fixing parameter values
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6.2.3 Estimating g0
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6.2.4 Estimating calling probability
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7 Plotting results
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8 Model selection
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8.1 AIC
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8.2 Model plausibility
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10 Reporting bugs
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